The Covid-19 pandemic has accelerated change in consumer behaviour; for example, online purchases are at their highest while the frequency of purchases are low.
Most retailers are aware that consumers will not revert back to their pre-pandemic ways when brick-and-mortar stores reopen on April 12th. Therefore, most have taken measures to prepare for their customers current needs: contact free payments, immersive shopping experiences, click-and-collect, in-store rewards and many more.
In order to recover from the pandemic, retailers must use data to understand customers’ needs and tailor shopping experiences accordingly. McKinsey’s survey found a correlation between customer analytics and corporate performance; it states, “50% of customer analytics champions are likely to have sales well above their competitors” making analytics a necessity at a time when businesses are having to rethink strategy to cater to the new consumer demands.
Before we jump to the role of customer analytics in corporate performance, here is the type of data current offline analytics tools can provide:
How can this data benefit you?
Retailers are working hard to understand their customers’ behaviour and spending money to make their brand relevant; some making small changes while others are completely redesigning strategy. However, rather than making decisions based on speculations, retailers must harness the power of data to work out the specific needs of their customers and adapt businesses accordingly.
Modern software analytics tools can tell you footfall with heat maps of hot and cold areas, as well as most popular routes taken. This information reveals how effective your efforts of attracting customers are, and when inside, how effective product placements and in-store promotions are.
Digging deeper into the customer psyche, you can analyse how long customers look at a product versus conversion. What are the return rates if they do end up purchasing? Is it the pricing that needs to be amended or the product quality? This is vital information, for new and existing products, which can be used during the development stage of a new product, pricing, merchandising etc.
Another example is bounce rate. Is the issue the lack of sales staff or queuing time? What is the average time customers spend in store, at different times of the day? How long are they willing to queue before abandoning the shopping cart? How can you command more attention from your customer?
At a time where 90% of UK consumers are expecting to do most of their shopping online while using physical stores as fulfilment centres, it is more important than ever to analyse the role of your stores and meet customer demands. Omnichannel retailers have further obligation to make omnichannel experience seamless.
Actionable data will be the wise friend retailers seek for help. Only by understanding your customers’ needs will you be able to refine their experiences. Reinvigorating customers by aligning your brand with their needs will be the key to high street revival.
This level of connection is near impossible to achieve in the digital space since such immersive experience cannot be replicated online.
Customer analytics is one of the key drivers of effective marketing. To attract new customers and reengage existing ones, retailers will need to understand their customers’ behaviour and target them with appropriate marketing tools at the right moment.
Measuring, for example, the effect of your window displays on shoppers. How long did they observe the display before entering? How many didn’t enter the store after seeing the display? Is the data different for different demographics and at different times of the day or week? Having answers to these questions will help retailers improve footfall through the use of effective window displays.
Once the customer is inside the store, further marketing efforts are made to help guide them through the shopping journey. Knowing your customers’ likes and dislikes will help you promote the right products and place promotions in the optimal way.
To stay competitive and relevant, many retailers have changed marketing tactics. So, in order to figure out which marketing is effective on specific buyer personas, rely on data to give you an accurate answer. By precisely targeting your marketing towards customers, retailers can improve conversion and cut costs at the same time.
The McKinsey survey found significant improvement in overall business performance when c-suite management actively use data analytics to make operational decisions. The good news is, modern analytics tools are designed to be highly comprehensible to all staff, with or without tech skills. Therefore, you should strive to make all decisions- sales, marketing, development, operational etc – backed by data in order to have high impact.
For example, customers often leave stores without purchasing anything when the queues are too long or they are not receiving the right sales attention. This can be rectified with data which can predict footfall and therefore the number of staff required. With this simple solution, conversion rate is improved through customer satisfaction and staff morale is also high when operations run smoothly.
With many retail stores turning into mini fulfilment centres, only looking at conversion is not a viable way to measure the benefit of the store. In this case, it is imperative that you know what your customers are using the store for: is it product discovery, to interact with products, or to engaging with the brand? The most important data to measure is engagement, which many retailers currently don’t measure in brick-and-mortar stores, slowing their growth in comparison to online stores.
Furthermore, once you’ve established the role of a store (and this could differ from store to store within the same brand) in the customers’ journey, you can decide if it is adding value or draining resources. Making an informed decision on whether to keep a store open or close it permanently should be backed by data, which can tell you if the store still plays a role in the customers’ journey despite low conversions. Similarly, when opening a new store, data provides insights into what the demographics of that location demands from your brand.
Starbucks is a noteworthy example as the brand increased its revenue by 26% between 2016 and 2019 having used data to work out profitable locations and engage customers with meaningful marketing promotions.
The more analytics you use, the more in-depth knowledge you will have on your customers. Since the machine learning algorithm is self-learning, it will also improve accuracy overtime.
You will be able to accurately anticipate future demand and trends, as well as have the competitive edge when reacting to them and positioning yourself as the solution provider before.
In-store, you will know which products and marketing tools have the highest impact so you can predict the success of future marketing campaigns and develop products that are in demand.
In summary, the pandemic caused rapid change in consumer needs causing retailers to rethink strategy. The changes made by retailers thus far have been based on speculations, but unfortunately, retailers do not have the time to trial and error new operational strategies if they want to recover quickly.
The solution comes from data. It would be silly to run your e-commerce site without data. Equally, in 2021, it’s important you gather the same, if not better, insights to optimise store performance.
There are many opportunities for retailers to bounce back quickly. Using data to identify exactly what your customers need and reacting to that is the most reliable way to achieve results.
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